Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	127,153
2	South Dakota	121,154
3	Rhode Island	105,491
4	Utah	105,199
5	Tennessee	101,906
6	Arizona	100,049
7	Wisconsin	100,040
8	Iowa	99,492
9	Nebraska	97,183
10	Oklahoma	94,732
11	Arkansas	94,340
12	Kansas	93,439
13	Indiana	91,546
14	Alabama	90,351
15	Idaho	89,634
16	Mississippi	89,090
17	Nevada	88,313
18	Wyoming	88,226
19	Illinois	87,389
20	Montana	85,990
21	Louisiana	83,019
22	South Carolina	81,848
23	California	81,303
24	New Mexico	80,929
25	Minnesota	80,829
26	Georgia	79,777
27	Missouri	78,845
28	Kentucky	78,667
29	Texas	78,300
30	Delaware	77,216
31	Florida	77,203
32	New Jersey	75,088
33	Ohio	74,313
34	Massachusetts	73,077
35	Alaska	72,532
36	New York	69,289
37	North Carolina	69,142
38	Connecticut	68,334
39	Colorado	67,588
40	West Virginia	64,636
41	Pennsylvania	63,557
42	Michigan	59,923
43	Maryland	56,793
44	Virginia	56,073
45	District of Columbia	50,308
46	New Hampshire	46,162
47	Washington	40,248
48	Puerto Rico	39,205
49	Oregon	32,858
50	Maine	27,559
51	Vermont	17,892
52	Hawaii	17,804

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	822
2	South Carolina	791
3	Oklahoma	746
4	Delaware	670
5	New York	655
6	Rhode Island	635
7	New Jersey	621
8	California	603
9	Massachusetts	591
10	Virginia	580
11	Georgia	565
12	Connecticut	543
13	North Carolina	516
14	Kentucky	514
15	Texas	483
16	Florida	474
17	Alabama	470
18	Mississippi	445
19	New Hampshire	445
20	Utah	431
21	Ohio	418
22	West Virginia	413
23	Louisiana	407
24	Nevada	396
25	Indiana	386
26	Arkansas	366
27	Pennsylvania	357
28	Tennessee	349
29	Maryland	343
30	New Mexico	312
31	Illinois	301
32	Kansas	298
33	Missouri	292
34	District of Columbia	283
35	Wyoming	275
36	Wisconsin	256
37	Puerto Rico	241
38	Montana	237
39	Colorado	235
40	Iowa	233
41	Nebraska	232
42	Alaska	222
43	Idaho	220
44	Vermont	216
45	Minnesota	207
46	Maine	191
47	South Dakota	174
48	Washington	172
49	Michigan	157
50	Oregon	138
51	North Dakota	134
52	Hawaii	90

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,361
2	New York	2,163
3	Massachusetts	2,057
4	Rhode Island	1,991
5	Mississippi	1,941
6	Connecticut	1,938
7	South Dakota	1,927
8	North Dakota	1,884
9	Louisiana	1,847
10	Arizona	1,681
11	Illinois	1,637
12	Pennsylvania	1,614
13	Arkansas	1,540
14	Michigan	1,522
15	New Mexico	1,506
16	Indiana	1,444
17	Iowa	1,422
18	Alabama	1,358
19	Nevada	1,309
20	Tennessee	1,285
21	South Carolina	1,272
22	District of Columbia	1,245
23	Kansas	1,243
24	Texas	1,215
25	Georgia	1,214
26	Florida	1,184
27	Maryland	1,141
28	Missouri	1,135
29	Minnesota	1,093
30	Delaware	1,077
31	Montana	1,077
32	Wisconsin	1,065
33	West Virginia	1,059
34	Nebraska	1,019
35	Wyoming	986
36	Colorado	972
37	California	949
38	Idaho	944
39	Ohio	921
40	North Carolina	836
41	Kentucky	834
42	Oklahoma	832
43	New Hampshire	728
44	Virginia	712
45	Puerto Rico	556
46	Washington	554
47	Utah	498
48	Oregon	448
49	Maine	406
50	Alaska	341
51	Vermont	274
52	Hawaii	239

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Montana	14
2	New Mexico	12
3	Alabama	11
4	Arkansas	11
5	Arizona	10
6	California	9
7	Massachusetts	9
8	New York	9
9	South Carolina	9
10	Connecticut	8
11	Michigan	8
12	North Carolina	8
13	Oklahoma	8
14	Pennsylvania	8
15	Rhode Island	8
16	Texas	8
17	Delaware	7
18	Georgia	7
19	Kentucky	7
20	Louisiana	7
21	Mississippi	7
22	Nevada	7
23	South Dakota	7
24	West Virginia	7
25	Florida	6
26	District of Columbia	5
27	Illinois	5
28	Maryland	5
29	Indiana	4
30	Nebraska	4
31	New Hampshire	4
32	Ohio	4
33	Colorado	3
34	Idaho	3
35	Minnesota	3
36	New Jersey	3
37	North Dakota	3
38	Tennessee	3
39	Virginia	3
40	Wisconsin	3
41	Alaska	2
42	Hawaii	2
43	Kansas	2
44	Utah	2
45	Iowa	1
46	Maine	1
47	Missouri	1
48	Oregon	1
49	Puerto Rico	1
50	Vermont	1
51	Washington	1
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	291,041	1	99
Dewey	South Dakota	234,895	2	99
Lincoln	Arkansas	230,190	3	99
Bent	Colorado	230,052	4	99
Chattahoochee	Georgia	228,936	5	99
Davidson	Tennessee	119,364	242	92
Richland	South Carolina	82,180	1401	55
York	South Carolina	75,280	1763	43
Orange	California	74,852	1779	43
Pierce	Washington	37,491	2889	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,452	4	99
Grant	Nebraska	6,421	5	99
Davidson	Tennessee	998	1907	39
Richland	South Carolina	948	1983	36
Orange	California	851	2121	32
York	South Carolina	758	2265	27
Pierce	Washington	493	2644	15

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.